{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T09:41:19Z","timestamp":1772271679482,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Cortisol and testosterone are key digital biomarkers reflecting neuroendocrine activity across the hypothalamic\u2013pituitary\u2013adrenal (HPA) and hypothalamic\u2013pituitary\u2013gonadal (HPG) axes, encoding stress adaptation and behavioral regulation. Continuous real-world monitoring remains challenging due to the sparsity of sensing and the complexity of multimodal data. This study introduces a synthetic sensor-driven computational framework that models hormone variability through data-driven simulation and predictive learning, eliminating the need for continuous biosensor input. A hybrid deep ensemble integrates biological, behavioral, and contextual data using bidirectional multitask learning with one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) branches, meta-gated expert fusion, Bayesian variational layers with Monte Carlo Dropout, and adversarial debiasing. Synthetically derived longitudinal hormone profiles that were validated by Kolmogorov\u2013Smirnov (KS), Wasserstein, maximum mean discrepancy (MMD), and dynamic time warping (DTW) metrics account for class imbalance and temporal sparsity. Our framework achieved up to 99.99% macro F1-score on augmented samples and more than 97% for unseen data with ECE below 0.001. Selective prediction further maximized the convergence of predictions for low-confidence cases, achieving 99.9992\u201399.9998% accuracy on 99.5% of samples, which were smaller than 5 MB in size so that they can be employed in real time when mounted on wearable devices. Explainability investigations revealed the most important features on both the physiological and behavioral levels, demonstrating framework capabilities for adaptive clinical or organizational stress monitoring.<\/jats:p>","DOI":"10.3390\/computers14120515","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T16:31:54Z","timestamp":1764088314000},"page":"515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fair and Explainable Multitask Deep Learning on Synthetic Endocrine Trajectories for Real-Time Prediction of Stress, Performance, and Neuroendocrine States"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7983-2189","authenticated-orcid":false,"family":"Abdullah","sequence":"first","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07738, Mexico"},{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6154-1893","authenticated-orcid":false,"given":"Zulaikha","family":"Fatima","sequence":"additional","affiliation":[{"name":"Faculty of Allied Health Sciences, Superior University, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6935-2870","authenticated-orcid":false,"given":"Carlos Guzman","family":"S\u00e1nchez Mejorada","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5397-6768","authenticated-orcid":false,"given":"Muhammad Ateeb","family":"Ather","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8308-8882","authenticated-orcid":false,"given":"Jos\u00e9 Luis","family":"Oropeza Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3901-3522","authenticated-orcid":false,"given":"Grigori","family":"Sidorov","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mohd Azmi, N.A.S., Juliana, N., Azmani, S., Mohd Effendy, N., Abu, I.F., Mohd Fahmi Teng, N.I., and Das, S. 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